Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.

Abstract

Jerome Cornfield's (1959) analysis of smoking and lung cancer introduced a foundational principle for causal inference from observational data: causal conclusions should be evaluated not by the absence of assumption violations, but by the implausibility of the violations required to overturn them. In the decades since, causal inference research has made substantial progress in explicitly defining identification assumptions and improving estimation under those assumptions. As estimation theory has advanced, concerns about model misspecification have receded, and flexible semi-parametric methods now exist for increasingly complex causal questions. Despite this progress, there has been comparatively little success in extending Cornfield-style sensitivity analysis to these modern settings, limiting both the interpretability and practical adoption of contemporary causal methods. In this commentary, we revisit Cornfield's original framework and consider its amenability for four identification assumptions that have received growing attention beyond standard unmeasured confounding: positivity, interference, parallel trends, and sequential exchangeability. For each, we evaluate its accessibility to Cornfield-style reasoning, discussing challenges and opportunities for developing interpretable, calibrated sensitivity analyses. We argue that Cornfield-style reasoning is more naturally applicable to some assumptions than others, clarifying where such approaches are promising and where fundamental obstacles remain.